import folium
import requests
import pandas
from folium.plugins import HeatMap

df = pandas.read_csv('Crime_Incidents_February_2017_to_Present.csv')

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 98160 entries, 0 to 98159
Data columns (total 11 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   Incident_case_id         98160 non-null  object 
 1   Date                     98160 non-null  object 
 2   Clearance_code_inc_type  98160 non-null  object 
 3   PGPD Reporting Area      98160 non-null  int64  
 4   PGPD Sector              98159 non-null  object 
 5   PGPD Beat                98158 non-null  object 
 6   Street_Number            75906 non-null  object 
 7   Street_Address           98160 non-null  object 
 8   Latitude                 98160 non-null  float64
 9   Longitude                98160 non-null  float64
 10  Location                 98160 non-null  object 
dtypes: float64(2), int64(1), object(8)
memory usage: 8.2+ MB

df.head()

	Incident_case_id	Date	Clearance_code_inc_type	PGPD Reporting Area	PGPD Sector	PGPD Beat	Street_Number	Street_Address	Latitude	Longitude	Location
0	PP20050900000816	05/09/2020	THEFT FROM AUTO	504	TRU	K1	NaN	23RD PKY SB / IVERSON ST WB	38.832853	-76.960127	(38.8328533917665, -76.9601267725229)
1	PP17082200000593	08/22/2017	THEFT FROM AUTO	709	E	E4	2100 BLOCK	2143 ROBERT BOWIE DR	38.862053	-76.794117	(38.8620528131723, -76.7941166162491)
2	PP19090100001543	09/01/2019	ACCIDENT WITH IMPOUND	710	E	E5	1300 BLOCK	1305 WHISTLING DUCK DR	38.872223	-76.770167	(38.8722233921289, -76.7701665312052)
3	PP17062000002184	06/20/2017	ACCIDENT	514	J	J1	4900 BLOCK	4950 INDIAN HEAD HWY NB	38.820386	-76.999825	(38.8203862309456, -76.9998248666525)
4	PP19081200001812	08/12/2019	THEFT FROM AUTO	111	A	A6	NaN	25TH AVE / KIRSTON ST	38.986724	-76.967345	(38.9867244809866, -76.9673446118832)

# getting a look at the different crimes codes
sorted(df['Clearance_code_inc_type'].unique())

['ACCIDENT',
 'ACCIDENT WITH IMPOUND',
 'ASSAULT',
 'ASSAULT, SHOOTING',
 'ASSAULT, WEAPON',
 'AUTO, STOLEN',
 'AUTO, STOLEN & RECOVERED',
 'B & E, COMMERCIAL',
 'B & E, OTHER',
 'B & E, RESIDENTIAL',
 'B & E, RESIDENTIAL (VACANT)',
 'B & E, SCHOOL',
 'B & E, VACANT',
 'HOMICIDE',
 'ROBBERY, COMMERCIAL',
 'ROBBERY, OTHER',
 'ROBBERY, RESIDENTIAL',
 'ROBBERY, VEHICLE',
 'SEX OFFENSE',
 'THEFT',
 'THEFT FROM AUTO',
 'VANDALISM']

df['Crime Category'] = df.apply(lambda row: row['Clearance_code_inc_type'].split(',')[0], axis=1)
df.head()

	Incident_case_id	Date	Clearance_code_inc_type	PGPD Reporting Area	PGPD Sector	PGPD Beat	Street_Number	Street_Address	Latitude	Longitude	Location	Crime Category
0	PP20050900000816	05/09/2020	THEFT FROM AUTO	504	TRU	K1	NaN	23RD PKY SB / IVERSON ST WB	38.832853	-76.960127	(38.8328533917665, -76.9601267725229)	THEFT FROM AUTO
1	PP17082200000593	08/22/2017	THEFT FROM AUTO	709	E	E4	2100 BLOCK	2143 ROBERT BOWIE DR	38.862053	-76.794117	(38.8620528131723, -76.7941166162491)	THEFT FROM AUTO
2	PP19090100001543	09/01/2019	ACCIDENT WITH IMPOUND	710	E	E5	1300 BLOCK	1305 WHISTLING DUCK DR	38.872223	-76.770167	(38.8722233921289, -76.7701665312052)	ACCIDENT WITH IMPOUND
3	PP17062000002184	06/20/2017	ACCIDENT	514	J	J1	4900 BLOCK	4950 INDIAN HEAD HWY NB	38.820386	-76.999825	(38.8203862309456, -76.9998248666525)	ACCIDENT
4	PP19081200001812	08/12/2019	THEFT FROM AUTO	111	A	A6	NaN	25TH AVE / KIRSTON ST	38.986724	-76.967345	(38.9867244809866, -76.9673446118832)	THEFT FROM AUTO

Map 1 - Color Coded Crime in College Park

crimes = sorted(df['Crime Category'].unique())
crimes

['ACCIDENT',
 'ACCIDENT WITH IMPOUND',
 'ASSAULT',
 'AUTO',
 'B & E',
 'HOMICIDE',
 'ROBBERY',
 'SEX OFFENSE',
 'THEFT',
 'THEFT FROM AUTO',
 'VANDALISM']

colors = [
    'purple',
    'darkpurple',
    'red',
    'orange',
    'beige',
    'white',
    'green',
    'darkgreen',
    'darkblue',
    'lightblue',
    'pink',
]

colors = dict(zip(crimes, colors))

# 38.97 < lat < 39.03
# -76.97 < lon < -76.90

data = df.copy()

data = data[data['Latitude']>=38.97]
data = data[data['Latitude']<=39.03]
data = data[data['Longitude']>=-76.97]
data = data[data['Longitude']<=-76.90]

map1 = folium.Map(location=[38.986, -76.94], zoom_start=14)

for date, code, lat, lon, cat in zip(data['Date'], data['Clearance_code_inc_type'], data['Latitude'], data['Longitude'], data['Crime Category']):
    folium.CircleMarker(
        location=[lat, lon],
        popup=(
            'Date: ' + str(date) + '<br>'
            'Crime: ' + str(code) + '<br>'
            ),
        color=colors[cat],
        fill_color=colors[cat]
    ).add_to(map1)

map1

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Map 2 - Breaking and Entering Heatmap

data = df[df['Clearance_code_inc_type']=='B & E, RESIDENTIAL']

map2 = folium.Map(location=[38.986, -76.94], zoom_start=14)

HeatMap(
    data=data[['Latitude', 'Longitude']],
    radius=15,
).add_to(map2)

map2

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Map 3 - GeoJSON Demo

CP_geo_link = "http://polygons.openstreetmap.fr/get_geojson.py?id=133393&params=0"

test_map = folium.Map(location=[38.986, -76.94], zoom_start=13)

folium.Choropleth(
    geo_data=CP_geo_link,
    name="choropleth"
).add_to(test_map)

test_map

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